Decoding Neurological Disease from MRI Brain Patterns

Event Abstract Back to Event Decoding Neurological Disease from MRI Brain Patterns Kerstin Hackmack1*, Martin Weygandt1 and John-Dylan Haynes1, 2 1 Bernstein Center for Computational Neuroscience, Germany 2 University of Munich, Germany Recently, pattern recognition approaches have been successfully applied in the field of clinical neuroimaging in order to differentiate between clinical groups [1]. Against this background, we present a fully automated procedure using local brain tissue characteristics of structural brain images for the prediction of the subjects’ clinical condition. We proceeded as follows. After segmenting the images into grey and white matter we applied a first statistical analysis referred to as voxel-based morphometry [2,3]. Here, standard statistical procedures are employed to make a voxel-wise comparison of the local concentration of grey and white matter between clinical groups. The result is a statistical parametric map indicating differences between these groups. In order to classify the segmented images into patient or control group, we used a two-stage procedure. In the first step, independent classifiers are trained on local brain patterns using a searchlight approach [4,5]. By employing a nested cross-validation scheme we obtained accuracy maps for each region in the brain. In the second step, we used an ensemble approach to combine the information of best discriminating (i.e. most informative) brain regions in order to make a final decision towards the clinical status for a novel image. The ensemble-method was chosen, since it has been shown that classifier-ensembles tend to have better generalization abilities compared to individual classifiers [6]. To predict symptom severity, a further regression analysis within the clinical group with respect to different clinical markers was included. To our best knowledge this is the first pattern recognition approach that combines local tissue characteristics and ensemble methods to decode clinical status. Because multivariate decoding algorithms are sensitive to regional pattern changes and therefore provide more information than univariate methods, the identification of new regions accompanying neurological disease seem to be conceivable and thus enable clinical applications.Acknowledgements:This work was funded by the German Research Foundation, the Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research and the Max Planck Society. References 1. Klöppel, S. et al., 2008. Brain, 131, 681-6892. Ashburner, J. et al., 2000. NeuroImage, 11, 805-8213. Good, C.D. et al., 2001. NeuroImage, 14, 21–364. Haynes, J.D. et al., 2007. Curr Biol, 17, 323-3285. Kriegeskorte, N. et al., 2006. Proc. Natl Acad. Sci. USA, 103, 3863–38686. Martinez-Ramon, M. et al., 2006. NeuroImage, 31, 1129-1141 Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Neurotechnology and brain computer interfaces Citation: Hackmack K, Weygandt M and Haynes J (2009). Decoding Neurological Disease from MRI Brain Patterns. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.112 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Aug 2009; Published Online: 27 Aug 2009. * Correspondence: Kerstin Hackmack, Bernstein Center for Computational Neuroscience, 82152 Planegg-Martinsried, Germany, kerstin.hackmack@bccn-berlin.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kerstin Hackmack Martin Weygandt John-Dylan Haynes Google Kerstin Hackmack Martin Weygandt John-Dylan Haynes Google Scholar Kerstin Hackmack Martin Weygandt John-Dylan Haynes PubMed Kerstin Hackmack Martin Weygandt John-Dylan Haynes Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.